Unbalanced urban development causes complex and diverse urban traffic conditions, which complicates microcirculation traffic network planning. To address this, a method based on fast search random tree algorithm is proposed. An urban microcirculation traffic network is constructed using directed graphs, and road network interference intensity and capacity are calculated. The interpolation collision detection method is used to determine the shortest path while considering constraint conditions. By incorporating target gravity into the RRT algorithm, a growth guidance function is obtained, optimising the planned path and completing urban microcirculation traffic network planning. Experimental results demonstrate accurate shortest path calculation with up to 11% delay reduction compared to existing methods. Energy consumption during planning is lower than 10 kJ, ensuring fair resource distribution within the urban microcirculation transportation network. These advantages highlight the practicality and effectiveness of this research method.